Overview

Brought to you by YData

Dataset statistics

Number of variables53
Number of observations8898
Missing cells12479
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 MiB
Average record size in memory1.0 KiB

Variable types

Numeric10
Categorical35
Text7
DateTime1

Alerts

CircuitID is highly overall correlated with Round and 3 other fieldsHigh correlation
ConstructorID is highly overall correlated with ConstructorName and 2 other fieldsHigh correlation
ConstructorName is highly overall correlated with ConstructorID and 2 other fieldsHigh correlation
PermanentNumber is highly overall correlated with Season and 1 other fieldsHigh correlation
Position is highly overall correlated with avg_constructor_positionHigh correlation
Round is highly overall correlated with CircuitIDHigh correlation
Season is highly overall correlated with PermanentNumberHigh correlation
avg_Q1_time is highly overall correlated with CircuitID and 2 other fieldsHigh correlation
avg_Q2_time is highly overall correlated with CircuitID and 2 other fieldsHigh correlation
avg_Q3_time is highly overall correlated with CircuitID and 2 other fieldsHigh correlation
avg_constructor_position is highly overall correlated with ConstructorID and 3 other fieldsHigh correlation
constructor_nationality is highly overall correlated with ConstructorID and 2 other fieldsHigh correlation
driver_nationality is highly overall correlated with PermanentNumberHigh correlation
was_first_last_1 is highly overall correlated with was_first_last_2 and 1 other fieldsHigh correlation
was_first_last_2 is highly overall correlated with was_first_last_1 and 2 other fieldsHigh correlation
was_first_last_3 is highly overall correlated with was_first_last_1 and 3 other fieldsHigh correlation
was_first_last_4 is highly overall correlated with was_first_last_2 and 3 other fieldsHigh correlation
was_first_last_5 is highly overall correlated with was_first_last_3 and 2 other fieldsHigh correlation
was_top10_last_1 is highly overall correlated with was_top10_last_2 and 7 other fieldsHigh correlation
was_top10_last_2 is highly overall correlated with was_top10_last_1 and 14 other fieldsHigh correlation
was_top10_last_3 is highly overall correlated with was_top10_last_1 and 14 other fieldsHigh correlation
was_top10_last_4 is highly overall correlated with was_top10_last_1 and 14 other fieldsHigh correlation
was_top10_last_5 is highly overall correlated with was_top10_last_2 and 12 other fieldsHigh correlation
was_top15_last_1 is highly overall correlated with was_top10_last_1 and 5 other fieldsHigh correlation
was_top15_last_2 is highly overall correlated with was_top10_last_1 and 8 other fieldsHigh correlation
was_top15_last_3 is highly overall correlated with was_top10_last_2 and 10 other fieldsHigh correlation
was_top15_last_4 is highly overall correlated with was_top10_last_2 and 10 other fieldsHigh correlation
was_top15_last_5 is highly overall correlated with was_top10_last_3 and 7 other fieldsHigh correlation
was_top3_last_1 is highly overall correlated with was_top3_last_2 and 5 other fieldsHigh correlation
was_top3_last_2 is highly overall correlated with was_top3_last_1 and 8 other fieldsHigh correlation
was_top3_last_3 is highly overall correlated with was_top3_last_1 and 9 other fieldsHigh correlation
was_top3_last_4 is highly overall correlated with was_first_last_4 and 10 other fieldsHigh correlation
was_top3_last_5 is highly overall correlated with was_first_last_5 and 8 other fieldsHigh correlation
was_top5_last_1 is highly overall correlated with was_top10_last_1 and 7 other fieldsHigh correlation
was_top5_last_2 is highly overall correlated with was_top10_last_2 and 13 other fieldsHigh correlation
was_top5_last_3 is highly overall correlated with was_top10_last_2 and 15 other fieldsHigh correlation
was_top5_last_4 is highly overall correlated with was_top10_last_3 and 14 other fieldsHigh correlation
was_top5_last_5 is highly overall correlated with was_top10_last_4 and 10 other fieldsHigh correlation
was_top8_last_1 is highly overall correlated with was_top10_last_1 and 8 other fieldsHigh correlation
was_top8_last_2 is highly overall correlated with was_top10_last_1 and 15 other fieldsHigh correlation
was_top8_last_3 is highly overall correlated with was_top10_last_2 and 15 other fieldsHigh correlation
was_top8_last_4 is highly overall correlated with was_top10_last_2 and 15 other fieldsHigh correlation
was_top8_last_5 is highly overall correlated with was_top10_last_2 and 14 other fieldsHigh correlation
was_first_last_1 is highly imbalanced (98.7%)Imbalance
was_first_last_2 is highly imbalanced (98.1%)Imbalance
was_first_last_3 is highly imbalanced (96.9%)Imbalance
was_first_last_4 is highly imbalanced (95.8%)Imbalance
was_first_last_5 is highly imbalanced (93.6%)Imbalance
was_top3_last_1 is highly imbalanced (95.9%)Imbalance
was_top3_last_2 is highly imbalanced (93.3%)Imbalance
was_top3_last_3 is highly imbalanced (90.3%)Imbalance
was_top3_last_4 is highly imbalanced (87.9%)Imbalance
was_top3_last_5 is highly imbalanced (84.5%)Imbalance
was_top5_last_1 is highly imbalanced (93.0%)Imbalance
was_top5_last_2 is highly imbalanced (88.7%)Imbalance
was_top5_last_3 is highly imbalanced (85.1%)Imbalance
was_top5_last_4 is highly imbalanced (81.2%)Imbalance
was_top5_last_5 is highly imbalanced (77.5%)Imbalance
was_top8_last_1 is highly imbalanced (91.3%)Imbalance
was_top8_last_2 is highly imbalanced (83.5%)Imbalance
was_top8_last_3 is highly imbalanced (78.0%)Imbalance
was_top8_last_4 is highly imbalanced (73.3%)Imbalance
was_top8_last_5 is highly imbalanced (68.6%)Imbalance
was_top10_last_1 is highly imbalanced (86.6%)Imbalance
was_top10_last_2 is highly imbalanced (77.5%)Imbalance
was_top10_last_3 is highly imbalanced (70.8%)Imbalance
was_top10_last_4 is highly imbalanced (65.5%)Imbalance
was_top10_last_5 is highly imbalanced (59.2%)Imbalance
was_top15_last_1 is highly imbalanced (77.9%)Imbalance
was_top15_last_2 is highly imbalanced (65.4%)Imbalance
was_top15_last_3 is highly imbalanced (57.2%)Imbalance
Code has 244 (2.7%) missing valuesMissing
avg_Q1_time has 2447 (27.5%) missing valuesMissing
avg_Q2_time has 2447 (27.5%) missing valuesMissing
avg_Q3_time has 2447 (27.5%) missing valuesMissing
driver_avg_position has 2447 (27.5%) missing valuesMissing
driver_total_races has 2447 (27.5%) missing valuesMissing
PermanentNumber has 2661 (29.9%) zerosZeros

Reproduction

Analysis started2024-08-13 00:03:36.204301
Analysis finished2024-08-13 00:03:51.886692
Duration15.68 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Season
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4229
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:51.951520image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2003
Q12008
median2013
Q32019
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.3321536
Coefficient of variation (CV)0.0031449695
Kurtosis-1.0907907
Mean2013.4229
Median Absolute Deviation (MAD)5
Skewness-0.070296226
Sum17915437
Variance40.096169
MonotonicityIncreasing
2024-08-12T21:03:52.080175image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2012 476
 
5.3%
2016 457
 
5.1%
2010 456
 
5.1%
2011 452
 
5.1%
2022 440
 
4.9%
2023 440
 
4.9%
2021 439
 
4.9%
2018 420
 
4.7%
2019 418
 
4.7%
2013 418
 
4.7%
Other values (15) 4482
50.4%
ValueCountFrequency (%)
2000 88
 
1.0%
2001 22
 
0.2%
2002 42
 
0.5%
2003 320
3.6%
2004 360
4.0%
2005 376
4.2%
2006 396
4.5%
2007 374
4.2%
2008 368
4.1%
2009 340
3.8%
ValueCountFrequency (%)
2024 279
3.1%
2023 440
4.9%
2022 440
4.9%
2021 439
4.9%
2020 340
3.8%
2019 418
4.7%
2018 420
4.7%
2017 398
4.5%
2016 457
5.1%
2015 374
4.2%

Round
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.063497
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:52.187886image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6762444
Coefficient of variation (CV)0.56404292
Kurtosis-1.1014975
Mean10.063497
Median Absolute Deviation (MAD)5
Skewness0.094492333
Sum89545
Variance32.219751
MonotonicityNot monotonic
2024-08-12T21:03:52.739313image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 485
 
5.5%
1 484
 
5.4%
4 484
 
5.4%
16 484
 
5.4%
3 483
 
5.4%
9 462
 
5.2%
11 462
 
5.2%
14 461
 
5.2%
12 461
 
5.2%
10 461
 
5.2%
Other values (12) 4171
46.9%
ValueCountFrequency (%)
1 484
5.4%
2 485
5.5%
3 483
5.4%
4 484
5.4%
5 458
5.1%
6 459
5.2%
7 460
5.2%
8 460
5.2%
9 462
5.2%
10 461
5.2%
ValueCountFrequency (%)
22 60
 
0.7%
21 122
 
1.4%
20 166
 
1.9%
19 293
3.3%
18 354
4.0%
17 438
4.9%
16 484
5.4%
15 441
5.0%
14 461
5.2%
13 460
5.2%

CircuitID
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size498.4 KiB
silverstone
 
503
hungaroring
 
462
catalunya
 
461
albert_park
 
443
monza
 
440
Other values (33)
6589 

Length

Max length14
Median length12
Mean length8.3417622
Min length3

Characters and Unicode

Total characters74225
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbert_park
2nd rowalbert_park
3rd rowalbert_park
4th rowalbert_park
5th rowalbert_park

Common Values

ValueCountFrequency (%)
silverstone 503
 
5.7%
hungaroring 462
 
5.2%
catalunya 461
 
5.2%
albert_park 443
 
5.0%
monza 440
 
4.9%
bahrain 439
 
4.9%
monaco 438
 
4.9%
suzuka 422
 
4.7%
spa 418
 
4.7%
interlagos 418
 
4.7%
Other values (28) 4454
50.1%

Length

2024-08-12T21:03:52.857997image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
silverstone 503
 
5.7%
hungaroring 462
 
5.2%
catalunya 461
 
5.2%
albert_park 443
 
5.0%
monza 440
 
4.9%
bahrain 439
 
4.9%
monaco 438
 
4.9%
suzuka 422
 
4.7%
interlagos 418
 
4.7%
spa 418
 
4.7%
Other values (28) 4454
50.1%

Most occurring characters

ValueCountFrequency (%)
a 10682
14.4%
n 7302
 
9.8%
r 6083
 
8.2%
i 5869
 
7.9%
e 4874
 
6.6%
s 4171
 
5.6%
o 4042
 
5.4%
l 3925
 
5.3%
u 3579
 
4.8%
g 3362
 
4.5%
Other values (14) 20336
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72479
97.6%
Connector Punctuation 1746
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10682
14.7%
n 7302
 
10.1%
r 6083
 
8.4%
i 5869
 
8.1%
e 4874
 
6.7%
s 4171
 
5.8%
o 4042
 
5.6%
l 3925
 
5.4%
u 3579
 
4.9%
g 3362
 
4.6%
Other values (13) 18590
25.6%
Connector Punctuation
ValueCountFrequency (%)
_ 1746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72479
97.6%
Common 1746
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10682
14.7%
n 7302
 
10.1%
r 6083
 
8.4%
i 5869
 
8.1%
e 4874
 
6.7%
s 4171
 
5.8%
o 4042
 
5.6%
l 3925
 
5.4%
u 3579
 
4.9%
g 3362
 
4.6%
Other values (13) 18590
25.6%
Common
ValueCountFrequency (%)
_ 1746
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10682
14.4%
n 7302
 
9.8%
r 6083
 
8.2%
i 5869
 
7.9%
e 4874
 
6.6%
s 4171
 
5.6%
o 4042
 
5.4%
l 3925
 
5.3%
u 3579
 
4.8%
g 3362
 
4.5%
Other values (14) 20336
27.4%
Distinct122
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size491.9 KiB
2024-08-12T21:03:53.080403image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.5915936
Min length3

Characters and Unicode

Total characters67550
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowhakkinen
2nd rowcoulthard
3rd rowmichael_schumacher
4th rowbarrichello
5th rowfrentzen
ValueCountFrequency (%)
alonso 377
 
4.2%
hamilton 346
 
3.9%
raikkonen 321
 
3.6%
vettel 299
 
3.4%
perez 272
 
3.1%
button 263
 
3.0%
massa 257
 
2.9%
ricciardo 252
 
2.8%
bottas 237
 
2.7%
hulkenberg 219
 
2.5%
Other values (112) 6055
68.0%
2024-08-12T21:03:53.441971image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6535
 
9.7%
a 6526
 
9.7%
r 5280
 
7.8%
o 5222
 
7.7%
n 5115
 
7.6%
l 4777
 
7.1%
s 4591
 
6.8%
i 4590
 
6.8%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (17) 18687
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66727
98.8%
Connector Punctuation 823
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6535
 
9.8%
a 6526
 
9.8%
r 5280
 
7.9%
o 5222
 
7.8%
n 5115
 
7.7%
l 4777
 
7.2%
s 4591
 
6.9%
i 4590
 
6.9%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (16) 17864
26.8%
Connector Punctuation
ValueCountFrequency (%)
_ 823
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66727
98.8%
Common 823
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6535
 
9.8%
a 6526
 
9.8%
r 5280
 
7.9%
o 5222
 
7.8%
n 5115
 
7.7%
l 4777
 
7.2%
s 4591
 
6.9%
i 4590
 
6.9%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (16) 17864
26.8%
Common
ValueCountFrequency (%)
_ 823
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6535
 
9.7%
a 6526
 
9.7%
r 5280
 
7.8%
o 5222
 
7.7%
n 5115
 
7.6%
l 4777
 
7.1%
s 4591
 
6.8%
i 4590
 
6.8%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (17) 18687
27.7%

Code
Text

MISSING 

Distinct96
Distinct (%)1.1%
Missing244
Missing (%)2.7%
Memory size447.2 KiB
2024-08-12T21:03:53.660387image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25962
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowCOU
2nd rowMSC
3rd rowBAR
4th rowTRU
5th rowVIL
ValueCountFrequency (%)
alo 377
 
4.4%
ham 346
 
4.0%
rai 321
 
3.7%
vet 299
 
3.5%
per 272
 
3.1%
but 263
 
3.0%
ver 257
 
3.0%
mas 257
 
3.0%
ric 252
 
2.9%
bot 237
 
2.7%
Other values (86) 5773
66.7%
2024-08-12T21:03:53.994748image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25962
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 25962
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

PermanentNumber
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.627444
Minimum0
Maximum99
Zeros2661
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:54.125399image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q325
95-th percentile77
Maximum99
Range99
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.098525
Coefficient of variation (CV)1.2937107
Kurtosis2.649303
Mean18.627444
Median Absolute Deviation (MAD)10
Skewness1.781518
Sum165747
Variance580.7389
MonotonicityNot monotonic
2024-08-12T21:03:54.264399image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 2661
29.9%
14 377
 
4.2%
44 346
 
3.9%
22 343
 
3.9%
7 321
 
3.6%
5 299
 
3.4%
11 272
 
3.1%
6 267
 
3.0%
19 257
 
2.9%
3 252
 
2.8%
Other values (37) 3503
39.4%
ValueCountFrequency (%)
0 2661
29.9%
2 77
 
0.9%
3 252
 
2.8%
4 153
 
1.7%
5 299
 
3.4%
6 267
 
3.0%
7 321
 
3.6%
8 181
 
2.0%
9 118
 
1.3%
10 218
 
2.4%
ValueCountFrequency (%)
99 189
2.1%
98 13
 
0.1%
94 39
 
0.4%
89 1
 
< 0.1%
88 111
1.2%
81 36
 
0.4%
77 237
2.7%
63 118
1.3%
55 197
2.2%
53 5
 
0.1%
Distinct111
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
2024-08-12T21:03:54.496778image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.9587548
Min length3

Characters and Unicode

Total characters53021
Distinct characters55
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowMika
2nd rowDavid
3rd rowMichael
4th rowRubens
5th rowHeinz-Harald
ValueCountFrequency (%)
nico 425
 
4.8%
fernando 377
 
4.2%
lewis 346
 
3.9%
kimi 321
 
3.6%
sebastian 299
 
3.4%
felipe 297
 
3.3%
sergio 272
 
3.1%
jenson 263
 
3.0%
daniel 252
 
2.8%
valtteri 237
 
2.7%
Other values (101) 5809
65.3%
2024-08-12T21:03:54.837274image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 5900
 
11.1%
a 5797
 
10.9%
e 5242
 
9.9%
n 4606
 
8.7%
o 3497
 
6.6%
r 3302
 
6.2%
s 2394
 
4.5%
l 2242
 
4.2%
t 1900
 
3.6%
c 1478
 
2.8%
Other values (45) 16663
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43963
82.9%
Uppercase Letter 8978
 
16.9%
Dash Punctuation 80
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5900
13.4%
a 5797
13.2%
e 5242
11.9%
n 4606
10.5%
o 3497
8.0%
r 3302
7.5%
s 2394
 
5.4%
l 2242
 
5.1%
t 1900
 
4.3%
c 1478
 
3.4%
Other values (21) 7605
17.3%
Uppercase Letter
ValueCountFrequency (%)
J 789
 
8.8%
S 765
 
8.5%
N 739
 
8.2%
M 729
 
8.1%
L 683
 
7.6%
F 681
 
7.6%
K 620
 
6.9%
R 594
 
6.6%
C 500
 
5.6%
D 477
 
5.3%
Other values (13) 2401
26.7%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52941
99.8%
Common 80
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5900
 
11.1%
a 5797
 
10.9%
e 5242
 
9.9%
n 4606
 
8.7%
o 3497
 
6.6%
r 3302
 
6.2%
s 2394
 
4.5%
l 2242
 
4.2%
t 1900
 
3.6%
c 1478
 
2.8%
Other values (44) 16583
31.3%
Common
ValueCountFrequency (%)
- 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52814
99.6%
None 207
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5900
 
11.2%
a 5797
 
11.0%
e 5242
 
9.9%
n 4606
 
8.7%
o 3497
 
6.6%
r 3302
 
6.3%
s 2394
 
4.5%
l 2242
 
4.2%
t 1900
 
3.6%
c 1478
 
2.8%
Other values (39) 16456
31.2%
None
ValueCountFrequency (%)
é 103
49.8%
É 58
28.0%
ô 40
 
19.3%
ó 4
 
1.9%
á 1
 
0.5%
Å¡ 1
 
0.5%
Distinct119
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size511.4 KiB
2024-08-12T21:03:55.090597image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.2200494
Min length3

Characters and Unicode

Total characters64244
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowHäkkinen
2nd rowCoulthard
3rd rowSchumacher
4th rowBarrichello
5th rowFrentzen
ValueCountFrequency (%)
alonso 377
 
4.1%
hamilton 346
 
3.7%
räikkönen 321
 
3.5%
vettel 299
 
3.2%
pérez 272
 
2.9%
schumacher 265
 
2.9%
button 263
 
2.8%
massa 257
 
2.8%
ricciardo 252
 
2.7%
bottas 237
 
2.6%
Other values (117) 6358
68.8%
2024-08-12T21:03:55.426351image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5797
 
9.0%
a 5351
 
8.3%
o 4676
 
7.3%
n 4630
 
7.2%
l 4314
 
6.7%
i 4300
 
6.7%
r 4126
 
6.4%
s 3578
 
5.6%
t 3507
 
5.5%
c 2135
 
3.3%
Other values (45) 21830
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54855
85.4%
Uppercase Letter 8992
 
14.0%
Space Separator 349
 
0.5%
Other Punctuation 48
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5797
10.6%
a 5351
9.8%
o 4676
 
8.5%
n 4630
 
8.4%
l 4314
 
7.9%
i 4300
 
7.8%
r 4126
 
7.5%
s 3578
 
6.5%
t 3507
 
6.4%
c 2135
 
3.9%
Other values (19) 12441
22.7%
Uppercase Letter
ValueCountFrequency (%)
R 1012
11.3%
S 978
10.9%
B 826
9.2%
H 751
8.4%
M 707
7.9%
V 686
 
7.6%
P 602
 
6.7%
A 589
 
6.6%
G 583
 
6.5%
K 501
 
5.6%
Other values (13) 1757
19.5%
Other Punctuation
ValueCountFrequency (%)
. 28
58.3%
' 20
41.7%
Space Separator
ValueCountFrequency (%)
349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63847
99.4%
Common 397
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5797
 
9.1%
a 5351
 
8.4%
o 4676
 
7.3%
n 4630
 
7.3%
l 4314
 
6.8%
i 4300
 
6.7%
r 4126
 
6.5%
s 3578
 
5.6%
t 3507
 
5.5%
c 2135
 
3.3%
Other values (42) 21433
33.6%
Common
ValueCountFrequency (%)
349
87.9%
. 28
 
7.1%
' 20
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63040
98.1%
None 1204
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5797
 
9.2%
a 5351
 
8.5%
o 4676
 
7.4%
n 4630
 
7.3%
l 4314
 
6.8%
i 4300
 
6.8%
r 4126
 
6.5%
s 3578
 
5.7%
t 3507
 
5.6%
c 2135
 
3.4%
Other values (41) 20626
32.7%
None
ValueCountFrequency (%)
é 338
28.1%
ä 326
27.1%
ö 321
26.7%
ü 219
18.2%
Distinct122
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
Minimum1964-06-11 00:00:00
Maximum2005-05-08 00:00:00
2024-08-12T21:03:55.559993image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:55.700618image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

driver_nationality
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size487.9 KiB
German
1414 
British
1158 
Spanish
691 
Finnish
681 
French
676 
Other values (29)
4278 

Length

Max length13
Median length10
Mean length7.1329512
Min length4

Characters and Unicode

Total characters63469
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFinnish
2nd rowBritish
3rd rowGerman
4th rowBrazilian
5th rowGerman

Common Values

ValueCountFrequency (%)
German 1414
15.9%
British 1158
13.0%
Spanish 691
 
7.8%
Finnish 681
 
7.7%
French 676
 
7.6%
Brazilian 623
 
7.0%
Australian 490
 
5.5%
Italian 472
 
5.3%
Mexican 331
 
3.7%
Dutch 307
 
3.5%
Other values (24) 2055
23.1%

Length

2024-08-12T21:03:55.837251image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
german 1414
15.8%
british 1158
13.0%
spanish 691
 
7.7%
finnish 681
 
7.6%
french 676
 
7.6%
brazilian 623
 
7.0%
australian 490
 
5.5%
italian 472
 
5.3%
mexican 331
 
3.7%
dutch 307
 
3.4%
Other values (25) 2085
23.4%

Most occurring characters

ValueCountFrequency (%)
i 8368
13.2%
a 8195
12.9%
n 8195
12.9%
r 4617
 
7.3%
s 4571
 
7.2%
e 4098
 
6.5%
h 4057
 
6.4%
t 2551
 
4.0%
l 1939
 
3.1%
B 1843
 
2.9%
Other values (30) 15035
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54511
85.9%
Uppercase Letter 8928
 
14.1%
Space Separator 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 8368
15.4%
a 8195
15.0%
n 8195
15.0%
r 4617
8.5%
s 4571
8.4%
e 4098
7.5%
h 4057
7.4%
t 2551
 
4.7%
l 1939
 
3.6%
m 1546
 
2.8%
Other values (12) 6374
11.7%
Uppercase Letter
ValueCountFrequency (%)
B 1843
20.6%
G 1414
15.8%
F 1357
15.2%
S 842
9.4%
A 645
 
7.2%
I 556
 
6.2%
D 489
 
5.5%
M 473
 
5.3%
C 398
 
4.5%
J 292
 
3.3%
Other values (7) 619
 
6.9%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63439
> 99.9%
Common 30
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 8368
13.2%
a 8195
12.9%
n 8195
12.9%
r 4617
 
7.3%
s 4571
 
7.2%
e 4098
 
6.5%
h 4057
 
6.4%
t 2551
 
4.0%
l 1939
 
3.1%
B 1843
 
2.9%
Other values (29) 15005
23.7%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 8368
13.2%
a 8195
12.9%
n 8195
12.9%
r 4617
 
7.3%
s 4571
 
7.2%
e 4098
 
6.5%
h 4057
 
6.4%
t 2551
 
4.0%
l 1939
 
3.1%
B 1843
 
2.9%
Other values (30) 15035
23.7%

ConstructorID
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size490.8 KiB
ferrari
850 
williams
848 
mclaren
847 
red_bull
766 
mercedes
589 
Other values (33)
4998 

Length

Max length12
Median length11
Mean length7.4678579
Min length2

Characters and Unicode

Total characters66449
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmclaren
2nd rowmclaren
3rd rowferrari
4th rowferrari
5th rowjordan

Common Values

ValueCountFrequency (%)
ferrari 850
 
9.6%
williams 848
 
9.5%
mclaren 847
 
9.5%
red_bull 766
 
8.6%
mercedes 589
 
6.6%
toro_rosso 533
 
6.0%
renault 522
 
5.9%
sauber 499
 
5.6%
force_india 423
 
4.8%
haas 359
 
4.0%
Other values (28) 2662
29.9%

Length

2024-08-12T21:03:55.952475image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ferrari 850
 
9.6%
williams 848
 
9.5%
mclaren 847
 
9.5%
red_bull 766
 
8.6%
mercedes 589
 
6.6%
toro_rosso 533
 
6.0%
renault 522
 
5.9%
sauber 499
 
5.6%
force_india 423
 
4.8%
haas 359
 
4.0%
Other values (28) 2662
29.9%

Most occurring characters

ValueCountFrequency (%)
r 9099
13.7%
a 7917
11.9%
e 6226
 
9.4%
l 5360
 
8.1%
i 4694
 
7.1%
s 4269
 
6.4%
o 3845
 
5.8%
n 3042
 
4.6%
m 3036
 
4.6%
u 2679
 
4.0%
Other values (15) 16282
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63845
96.1%
Connector Punctuation 2414
 
3.6%
Decimal Number 190
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 9099
14.3%
a 7917
12.4%
e 6226
9.8%
l 5360
 
8.4%
i 4694
 
7.4%
s 4269
 
6.7%
o 3845
 
6.0%
n 3042
 
4.8%
m 3036
 
4.8%
u 2679
 
4.2%
Other values (13) 13678
21.4%
Connector Punctuation
ValueCountFrequency (%)
_ 2414
100.0%
Decimal Number
ValueCountFrequency (%)
1 190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63845
96.1%
Common 2604
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 9099
14.3%
a 7917
12.4%
e 6226
9.8%
l 5360
 
8.4%
i 4694
 
7.4%
s 4269
 
6.7%
o 3845
 
6.0%
n 3042
 
4.8%
m 3036
 
4.8%
u 2679
 
4.2%
Other values (13) 13678
21.4%
Common
ValueCountFrequency (%)
_ 2414
92.7%
1 190
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 9099
13.7%
a 7917
11.9%
e 6226
 
9.4%
l 5360
 
8.1%
i 4694
 
7.1%
s 4269
 
6.4%
o 3845
 
5.8%
n 3042
 
4.6%
m 3036
 
4.6%
u 2679
 
4.0%
Other values (15) 16282
24.5%

ConstructorName
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size496.4 KiB
Ferrari
850 
Williams
848 
McLaren
847 
Red Bull
766 
Mercedes
589 
Other values (33)
4998 

Length

Max length14
Median length12
Mean length8.1062036
Min length3

Characters and Unicode

Total characters72129
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMcLaren
2nd rowMcLaren
3rd rowFerrari
4th rowFerrari
5th rowJordan

Common Values

ValueCountFrequency (%)
Ferrari 850
 
9.6%
Williams 848
 
9.5%
McLaren 847
 
9.5%
Red Bull 766
 
8.6%
Mercedes 589
 
6.6%
Toro Rosso 533
 
6.0%
Renault 522
 
5.9%
Sauber 499
 
5.6%
Force India 423
 
4.8%
Haas F1 Team 359
 
4.0%
Other values (28) 2662
29.9%

Length

2024-08-12T21:03:56.073154image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ferrari 850
 
6.7%
williams 848
 
6.7%
mclaren 847
 
6.7%
red 766
 
6.1%
bull 766
 
6.1%
f1 701
 
5.6%
sauber 639
 
5.1%
mercedes 589
 
4.7%
team 547
 
4.3%
toro 533
 
4.2%
Other values (35) 5517
43.8%

Most occurring characters

ValueCountFrequency (%)
a 7621
 
10.6%
e 6980
 
9.7%
r 6933
 
9.6%
l 4283
 
5.9%
i 4261
 
5.9%
o 4259
 
5.9%
3705
 
5.1%
s 3642
 
5.0%
n 2966
 
4.1%
u 2745
 
3.8%
Other values (28) 24734
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53265
73.8%
Uppercase Letter 14422
 
20.0%
Space Separator 3705
 
5.1%
Decimal Number 737
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7621
14.3%
e 6980
13.1%
r 6933
13.0%
l 4283
8.0%
i 4261
8.0%
o 4259
8.0%
s 3642
6.8%
n 2966
 
5.6%
u 2745
 
5.2%
d 2124
 
4.0%
Other values (11) 7451
14.0%
Uppercase Letter
ValueCountFrequency (%)
R 2363
16.4%
M 2142
14.9%
F 2010
13.9%
T 1611
11.2%
B 1094
7.6%
L 1077
7.5%
W 988
6.9%
A 899
 
6.2%
S 759
 
5.3%
H 580
 
4.0%
Other values (5) 899
 
6.2%
Space Separator
ValueCountFrequency (%)
3705
100.0%
Decimal Number
ValueCountFrequency (%)
1 737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67687
93.8%
Common 4442
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7621
 
11.3%
e 6980
 
10.3%
r 6933
 
10.2%
l 4283
 
6.3%
i 4261
 
6.3%
o 4259
 
6.3%
s 3642
 
5.4%
n 2966
 
4.4%
u 2745
 
4.1%
R 2363
 
3.5%
Other values (26) 21634
32.0%
Common
ValueCountFrequency (%)
3705
83.4%
1 737
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7621
 
10.6%
e 6980
 
9.7%
r 6933
 
9.6%
l 4283
 
5.9%
i 4261
 
5.9%
o 4259
 
5.9%
3705
 
5.1%
s 3642
 
5.0%
n 2966
 
4.1%
u 2745
 
3.8%
Other values (28) 24734
34.3%

constructor_nationality
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size485.1 KiB
British
2471 
Italian
1707 
Austrian
766 
German
729 
Swiss
706 
Other values (9)
2519 

Length

Max length9
Median length8
Mean length6.815127
Min length5

Characters and Unicode

Total characters60641
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBritish
2nd rowBritish
3rd rowItalian
4th rowItalian
5th rowIrish

Common Values

ValueCountFrequency (%)
British 2471
27.8%
Italian 1707
19.2%
Austrian 766
 
8.6%
German 729
 
8.2%
Swiss 706
 
7.9%
French 692
 
7.8%
Japanese 434
 
4.9%
Indian 423
 
4.8%
American 359
 
4.0%
Malaysian 188
 
2.1%
Other values (4) 423
 
4.8%

Length

2024-08-12T21:03:56.194018image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
british 2471
27.8%
italian 1707
19.2%
austrian 766
 
8.6%
german 729
 
8.2%
swiss 706
 
7.9%
french 692
 
7.8%
japanese 434
 
4.9%
indian 423
 
4.8%
american 359
 
4.0%
malaysian 188
 
2.1%
Other values (4) 423
 
4.8%

Most occurring characters

ValueCountFrequency (%)
i 9472
15.6%
a 7384
12.2%
n 5982
9.9%
s 5798
9.6%
r 5137
8.5%
t 4986
8.2%
h 3440
 
5.7%
e 2648
 
4.4%
B 2471
 
4.1%
I 2250
 
3.7%
Other values (16) 11073
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51743
85.3%
Uppercase Letter 8898
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9472
18.3%
a 7384
14.3%
n 5982
11.6%
s 5798
11.2%
r 5137
9.9%
t 4986
9.6%
h 3440
 
6.6%
e 2648
 
5.1%
l 1895
 
3.7%
c 1093
 
2.1%
Other values (6) 3908
7.6%
Uppercase Letter
ValueCountFrequency (%)
B 2471
27.8%
I 2250
25.3%
A 1125
12.6%
S 821
 
9.2%
G 729
 
8.2%
F 692
 
7.8%
J 434
 
4.9%
M 188
 
2.1%
R 146
 
1.6%
D 42
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 60641
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9472
15.6%
a 7384
12.2%
n 5982
9.9%
s 5798
9.6%
r 5137
8.5%
t 4986
8.2%
h 3440
 
5.7%
e 2648
 
4.4%
B 2471
 
4.1%
I 2250
 
3.7%
Other values (16) 11073
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9472
15.6%
a 7384
12.2%
n 5982
9.9%
s 5798
9.6%
r 5137
8.5%
t 4986
8.2%
h 3440
 
5.7%
e 2648
 
4.4%
B 2471
 
4.1%
I 2250
 
3.7%
Other values (16) 11073
18.3%

Q1
Text

Distinct7916
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size494.6 KiB
2024-08-12T21:03:56.433889image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9040234
Min length1

Characters and Unicode

Total characters70330
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7111 ?
Unique (%)79.9%

Sample

1st row1:30.556
2nd row1:30.910
3rd row1:31.075
4th row1:31.102
5th row1:31.359
ValueCountFrequency (%)
0 122
 
1.4%
1:17.244 4
 
< 0.1%
1:22.043 3
 
< 0.1%
1:15.644 3
 
< 0.1%
1:17.086 3
 
< 0.1%
1:22.130 3
 
< 0.1%
1:23.578 3
 
< 0.1%
1:27.039 3
 
< 0.1%
1:15.746 3
 
< 0.1%
1:25.859 3
 
< 0.1%
Other values (7906) 8748
98.3%
2024-08-12T21:03:56.868003image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14420
20.5%
: 8776
12.5%
. 8776
12.5%
2 5933
8.4%
3 5920
8.4%
4 4620
 
6.6%
5 3971
 
5.6%
0 3905
 
5.6%
6 3644
 
5.2%
7 3536
 
5.0%
Other values (2) 6829
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52778
75.0%
Other Punctuation 17552
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14420
27.3%
2 5933
11.2%
3 5920
11.2%
4 4620
 
8.8%
5 3971
 
7.5%
0 3905
 
7.4%
6 3644
 
6.9%
7 3536
 
6.7%
8 3505
 
6.6%
9 3324
 
6.3%
Other Punctuation
ValueCountFrequency (%)
: 8776
50.0%
. 8776
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14420
20.5%
: 8776
12.5%
. 8776
12.5%
2 5933
8.4%
3 5920
8.4%
4 4620
 
6.6%
5 3971
 
5.6%
0 3905
 
5.6%
6 3644
 
5.2%
7 3536
 
5.0%
Other values (2) 6829
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14420
20.5%
: 8776
12.5%
. 8776
12.5%
2 5933
8.4%
3 5920
8.4%
4 4620
 
6.6%
5 3971
 
5.6%
0 3905
 
5.6%
6 3644
 
5.2%
7 3536
 
5.0%
Other values (2) 6829
9.7%

Q2
Text

Distinct5345
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Memory size473.6 KiB
2024-08-12T21:03:57.189144image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length5.483367
Min length1

Characters and Unicode

Total characters48791
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5011 ?
Unique (%)56.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 3199
36.0%
1:26.319 3
 
< 0.1%
1:17.166 3
 
< 0.1%
1:37.347 3
 
< 0.1%
1:38.417 3
 
< 0.1%
1:15.885 3
 
< 0.1%
1:33.416 3
 
< 0.1%
1:15.322 3
 
< 0.1%
1:31.010 3
 
< 0.1%
1:15.706 3
 
< 0.1%
Other values (5335) 5672
63.7%
2024-08-12T21:03:57.563243image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9385
19.2%
0 5727
11.7%
: 5699
11.7%
. 5699
11.7%
2 3919
8.0%
3 3751
 
7.7%
4 2969
 
6.1%
5 2630
 
5.4%
6 2377
 
4.9%
7 2336
 
4.8%
Other values (2) 4299
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37393
76.6%
Other Punctuation 11398
 
23.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9385
25.1%
0 5727
15.3%
2 3919
10.5%
3 3751
 
10.0%
4 2969
 
7.9%
5 2630
 
7.0%
6 2377
 
6.4%
7 2336
 
6.2%
8 2212
 
5.9%
9 2087
 
5.6%
Other Punctuation
ValueCountFrequency (%)
: 5699
50.0%
. 5699
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9385
19.2%
0 5727
11.7%
: 5699
11.7%
. 5699
11.7%
2 3919
8.0%
3 3751
 
7.7%
4 2969
 
6.1%
5 2630
 
5.4%
6 2377
 
4.9%
7 2336
 
4.8%
Other values (2) 4299
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9385
19.2%
0 5727
11.7%
: 5699
11.7%
. 5699
11.7%
2 3919
8.0%
3 3751
 
7.7%
4 2969
 
6.1%
5 2630
 
5.4%
6 2377
 
4.9%
7 2336
 
4.8%
Other values (2) 4299
8.8%

Q3
Text

Distinct3392
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size458.8 KiB
2024-08-12T21:03:57.807589image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length8
Median length1
Mean length3.7801753
Min length1

Characters and Unicode

Total characters33636
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3253 ?
Unique (%)36.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 5364
60.3%
1:14.970 3
 
< 0.1%
1:35.766 3
 
< 0.1%
1:45.503 3
 
< 0.1%
1:38.513 3
 
< 0.1%
1:31.478 3
 
< 0.1%
1:24.305 2
 
< 0.1%
1:26.973 2
 
< 0.1%
1:16.818 2
 
< 0.1%
1:47.362 2
 
< 0.1%
Other values (3382) 3511
39.5%
2024-08-12T21:03:58.164634image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6968
20.7%
1 5885
17.5%
: 3534
10.5%
. 3534
10.5%
2 2474
 
7.4%
3 2242
 
6.7%
4 1872
 
5.6%
5 1555
 
4.6%
7 1427
 
4.2%
6 1400
 
4.2%
Other values (2) 2745
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26568
79.0%
Other Punctuation 7068
 
21.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6968
26.2%
1 5885
22.2%
2 2474
 
9.3%
3 2242
 
8.4%
4 1872
 
7.0%
5 1555
 
5.9%
7 1427
 
5.4%
6 1400
 
5.3%
8 1373
 
5.2%
9 1372
 
5.2%
Other Punctuation
ValueCountFrequency (%)
: 3534
50.0%
. 3534
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33636
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6968
20.7%
1 5885
17.5%
: 3534
10.5%
. 3534
10.5%
2 2474
 
7.4%
3 2242
 
6.7%
4 1872
 
5.6%
5 1555
 
4.6%
7 1427
 
4.2%
6 1400
 
4.2%
Other values (2) 2745
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6968
20.7%
1 5885
17.5%
: 3534
10.5%
. 3534
10.5%
2 2474
 
7.4%
3 2242
 
6.7%
4 1872
 
5.6%
5 1555
 
4.6%
7 1427
 
4.2%
6 1400
 
4.2%
Other values (2) 2745
 
8.2%

avg_Q1_time
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1052
Distinct (%)16.3%
Missing2447
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean87.667378
Minimum64.45
Maximum127.341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:58.296291image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum64.45
5-th percentile71.5
Q178.030917
median87.089333
Q396.055667
95-th percentile108.322
Maximum127.341
Range62.891
Interquartile range (IQR)18.02475

Descriptive statistics

Standard deviation11.510407
Coefficient of variation (CV)0.13129635
Kurtosis-0.34675966
Mean87.667378
Median Absolute Deviation (MAD)9.04
Skewness0.36646528
Sum565542.26
Variance132.48946
MonotonicityNot monotonic
2024-08-12T21:03:58.423923image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.15471429 20
 
0.2%
89.86408333 20
 
0.2%
81.37571429 20
 
0.2%
91.07588889 19
 
0.2%
75.92041667 19
 
0.2%
84.7643 19
 
0.2%
74.66935714 19
 
0.2%
89.2246 18
 
0.2%
80.32994118 18
 
0.2%
79.91082353 18
 
0.2%
Other values (1042) 6261
70.4%
(Missing) 2447
 
27.5%
ValueCountFrequency (%)
64.45 5
 
0.1%
64.7875 5
 
0.1%
64.94175 10
0.1%
64.98133333 9
0.1%
65.072 8
0.1%
65.1152 13
0.1%
65.311 2
 
< 0.1%
65.57566667 8
0.1%
66.3044 11
0.1%
66.405 3
 
< 0.1%
ValueCountFrequency (%)
127.341 2
 
< 0.1%
126.115 2
 
< 0.1%
125.598 2
 
< 0.1%
125.419 3
< 0.1%
125.292 3
< 0.1%
125.047 3
< 0.1%
121.689 3
< 0.1%
120.387 7
0.1%
120.356 3
< 0.1%
119.431 1
 
< 0.1%

avg_Q2_time
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1052
Distinct (%)16.3%
Missing2447
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean86.965961
Minimum64.194
Maximum124.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:58.551583image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum64.194
5-th percentile70.7865
Q177.605
median86.6675
Q395.5125
95-th percentile106.16875
Maximum124.561
Range60.367
Interquartile range (IQR)17.9075

Descriptive statistics

Standard deviation11.204775
Coefficient of variation (CV)0.12884093
Kurtosis-0.56126144
Mean86.965961
Median Absolute Deviation (MAD)8.9815
Skewness0.26690925
Sum561017.41
Variance125.54699
MonotonicityNot monotonic
2024-08-12T21:03:58.682816image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.59378571 20
 
0.2%
89.82983333 20
 
0.2%
79.77357143 20
 
0.2%
74.63816667 19
 
0.2%
89.99244444 19
 
0.2%
73.839 19
 
0.2%
84.4073 19
 
0.2%
86.765125 18
 
0.2%
79.72529412 18
 
0.2%
109.414625 18
 
0.2%
Other values (1042) 6261
70.4%
(Missing) 2447
 
27.5%
ValueCountFrequency (%)
64.194 5
 
0.1%
64.5715 10
0.1%
64.5745 5
 
0.1%
64.5905 9
0.1%
64.6238 13
0.1%
64.689 8
0.1%
65.07 2
 
< 0.1%
65.08866667 8
0.1%
65.9264 11
0.1%
66.031 10
0.1%
ValueCountFrequency (%)
124.561 3
< 0.1%
124.452 3
< 0.1%
123.466 3
< 0.1%
122.096 3
< 0.1%
122.094 3
< 0.1%
116.584 3
< 0.1%
116.44 7
0.1%
114.278 2
 
< 0.1%
113.848 2
 
< 0.1%
113.657 2
 
< 0.1%

avg_Q3_time
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1051
Distinct (%)16.3%
Missing2447
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean87.270336
Minimum64.179
Maximum123.095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:58.809605image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum64.179
5-th percentile70.92
Q177.60225
median86.713
Q395.598
95-th percentile107.241
Maximum123.095
Range58.916
Interquartile range (IQR)17.99575

Descriptive statistics

Standard deviation11.431281
Coefficient of variation (CV)0.13098702
Kurtosis-0.50077745
Mean87.270336
Median Absolute Deviation (MAD)9.057
Skewness0.3150203
Sum562980.94
Variance130.6742
MonotonicityNot monotonic
2024-08-12T21:03:58.937966image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.52571429 20
 
0.2%
89.656 20
 
0.2%
79.67628571 20
 
0.2%
85.3978 19
 
0.2%
74.55491667 19
 
0.2%
73.82485714 19
 
0.2%
89.84011111 19
 
0.2%
86.294875 18
 
0.2%
79.36752941 18
 
0.2%
88.4566 18
 
0.2%
Other values (1041) 6261
70.4%
(Missing) 2447
 
27.5%
ValueCountFrequency (%)
64.179 5
 
0.1%
64.31566667 9
0.1%
64.3935 5
 
0.1%
64.562 10
0.1%
64.5666 13
0.1%
64.686 8
0.1%
64.954 8
0.1%
65.048 2
 
< 0.1%
65.87925 10
0.1%
66.011 3
 
< 0.1%
ValueCountFrequency (%)
123.095 4
 
< 0.1%
123.078 3
 
< 0.1%
122.213 3
 
< 0.1%
121.164 7
0.1%
119.566 9
0.1%
117.226 2
 
< 0.1%
116.773 3
 
< 0.1%
115.611 3
 
< 0.1%
115.5755 10
0.1%
114.9488 12
0.1%

driver_avg_position
Real number (ℝ)

MISSING 

Distinct158
Distinct (%)2.4%
Missing2447
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean5.7299963
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:59.064834image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q14.3333333
median5.6666667
Q37
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)2.6666667

Descriptive statistics

Standard deviation2.0269641
Coefficient of variation (CV)0.35374616
Kurtosis-0.36355871
Mean5.7299963
Median Absolute Deviation (MAD)1.3333333
Skewness0.098784786
Sum36964.206
Variance4.1085836
MonotonicityNot monotonic
2024-08-12T21:03:59.197848image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 414
 
4.7%
5 367
 
4.1%
6 329
 
3.7%
8 302
 
3.4%
9 239
 
2.7%
10 220
 
2.5%
6.5 215
 
2.4%
4 191
 
2.1%
7.5 172
 
1.9%
5.5 153
 
1.7%
Other values (148) 3849
43.3%
(Missing) 2447
27.5%
ValueCountFrequency (%)
1 52
0.6%
1.333333333 3
 
< 0.1%
1.5 23
 
0.3%
1.666666667 16
 
0.2%
1.777777778 12
 
0.1%
1.923076923 16
 
0.2%
2 70
0.8%
2.071428571 15
 
0.2%
2.111111111 11
 
0.1%
2.2 5
 
0.1%
ValueCountFrequency (%)
16 2
 
< 0.1%
10 220
2.5%
9.5 34
 
0.4%
9.333333333 20
 
0.2%
9.25 5
 
0.1%
9.166666667 11
 
0.1%
9 239
2.7%
8.75 23
 
0.3%
8.666666667 48
 
0.5%
8.5 80
 
0.9%

driver_total_races
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)0.3%
Missing2447
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean4.9882189
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:59.314425image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile12
Maximum18
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.803815
Coefficient of variation (CV)0.76255976
Kurtosis0.46425166
Mean4.9882189
Median Absolute Deviation (MAD)3
Skewness1.0196152
Sum32179
Variance14.469008
MonotonicityNot monotonic
2024-08-12T21:03:59.418975image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1260
14.2%
2 960
 
10.8%
3 776
 
8.7%
4 577
 
6.5%
5 479
 
5.4%
6 433
 
4.9%
8 420
 
4.7%
10 389
 
4.4%
7 376
 
4.2%
9 237
 
2.7%
Other values (8) 544
 
6.1%
(Missing) 2447
27.5%
ValueCountFrequency (%)
1 1260
14.2%
2 960
10.8%
3 776
8.7%
4 577
6.5%
5 479
 
5.4%
6 433
 
4.9%
7 376
 
4.2%
8 420
 
4.7%
9 237
 
2.7%
10 389
 
4.4%
ValueCountFrequency (%)
18 19
 
0.2%
17 53
 
0.6%
16 18
 
0.2%
15 49
 
0.6%
14 134
 
1.5%
13 32
 
0.4%
12 168
1.9%
11 71
 
0.8%
10 389
4.4%
9 237
2.7%

avg_constructor_position
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.020004
Minimum4.6893039
Maximum22.947826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:03:59.533668image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum4.6893039
5-th percentile4.6893039
Q16.3772846
median11.697368
Q313.731707
95-th percentile19.758929
Maximum22.947826
Range18.258522
Interquartile range (IQR)7.3544227

Descriptive statistics

Standard deviation4.2778933
Coefficient of variation (CV)0.38819342
Kurtosis-0.062450224
Mean11.020004
Median Absolute Deviation (MAD)2.6974212
Skewness0.51926409
Sum98056
Variance18.300371
MonotonicityNot monotonic
2024-08-12T21:03:59.662323image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5.827058824 850
 
9.6%
12.4009434 848
 
9.5%
8.459268005 847
 
9.5%
6.377284595 766
 
8.6%
4.689303905 589
 
6.6%
13.73170732 533
 
6.0%
9.938697318 522
 
5.9%
14.39478958 499
 
5.6%
12.04491726 423
 
4.8%
14.08077994 359
 
4.0%
Other values (28) 2662
29.9%
ValueCountFrequency (%)
4.689303905 589
6.6%
4.823529412 34
 
0.4%
5.827058824 850
9.6%
6.377284595 766
8.6%
8.459268005 847
9.5%
9.028571429 140
 
1.6%
9.344827586 116
 
1.3%
9.864 250
 
2.8%
9.938697318 522
5.9%
10.74675325 154
 
1.7%
ValueCountFrequency (%)
22.94782609 115
1.3%
21.31578947 76
0.9%
20.97058824 34
 
0.4%
20.27966102 118
1.3%
19.75892857 112
1.3%
19.375 8
 
0.1%
19.33333333 66
0.7%
19.19736842 76
0.9%
19.06410256 78
0.9%
18.56666667 120
1.3%

was_first_last_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8888 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8888
99.9%
1 10
 
0.1%

Length

2024-08-12T21:03:59.787010image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:59.896718image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8888
99.9%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 8888
99.9%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8888
99.9%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8888
99.9%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8888
99.9%
1 10
 
0.1%

was_first_last_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8882 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8882
99.8%
1 16
 
0.2%

Length

2024-08-12T21:04:00.021382image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:00.122113image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8882
99.8%
1 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 8882
99.8%
1 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8882
99.8%
1 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8882
99.8%
1 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8882
99.8%
1 16
 
0.2%

was_first_last_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8870 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8870
99.7%
1 28
 
0.3%

Length

2024-08-12T21:04:00.221846image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:00.317590image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8870
99.7%
1 28
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 8870
99.7%
1 28
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8870
99.7%
1 28
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8870
99.7%
1 28
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8870
99.7%
1 28
 
0.3%

was_first_last_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8857 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8857
99.5%
1 41
 
0.5%

Length

2024-08-12T21:04:00.438280image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:00.540007image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8857
99.5%
1 41
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 8857
99.5%
1 41
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8857
99.5%
1 41
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8857
99.5%
1 41
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8857
99.5%
1 41
 
0.5%

was_first_last_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8831 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8831
99.2%
1 67
 
0.8%

Length

2024-08-12T21:04:00.651709image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:00.749447image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8831
99.2%
1 67
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 8831
99.2%
1 67
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8831
99.2%
1 67
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8831
99.2%
1 67
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8831
99.2%
1 67
 
0.8%

was_top3_last_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8859 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8859
99.6%
1 39
 
0.4%

Length

2024-08-12T21:04:00.844724image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:00.937824image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8859
99.6%
1 39
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 8859
99.6%
1 39
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8859
99.6%
1 39
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8859
99.6%
1 39
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8859
99.6%
1 39
 
0.4%

was_top3_last_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8827 
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8827
99.2%
1 71
 
0.8%

Length

2024-08-12T21:04:01.037559image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:01.191149image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8827
99.2%
1 71
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 8827
99.2%
1 71
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8827
99.2%
1 71
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8827
99.2%
1 71
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8827
99.2%
1 71
 
0.8%

was_top3_last_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8786 
1
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8786
98.7%
1 112
 
1.3%

Length

2024-08-12T21:04:01.290288image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:01.385428image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8786
98.7%
1 112
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 8786
98.7%
1 112
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8786
98.7%
1 112
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8786
98.7%
1 112
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8786
98.7%
1 112
 
1.3%

was_top3_last_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8752 
1
 
146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8752
98.4%
1 146
 
1.6%

Length

2024-08-12T21:04:01.486159image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:01.588884image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8752
98.4%
1 146
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 8752
98.4%
1 146
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8752
98.4%
1 146
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8752
98.4%
1 146
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8752
98.4%
1 146
 
1.6%

was_top3_last_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8698 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8698
97.8%
1 200
 
2.2%

Length

2024-08-12T21:04:01.700278image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:01.793029image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8698
97.8%
1 200
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 8698
97.8%
1 200
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8698
97.8%
1 200
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8698
97.8%
1 200
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8698
97.8%
1 200
 
2.2%

was_top5_last_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8823 
1
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8823
99.2%
1 75
 
0.8%

Length

2024-08-12T21:04:01.890768image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:01.981524image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8823
99.2%
1 75
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 8823
99.2%
1 75
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8823
99.2%
1 75
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8823
99.2%
1 75
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8823
99.2%
1 75
 
0.8%

was_top5_last_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8763 
1
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8763
98.5%
1 135
 
1.5%

Length

2024-08-12T21:04:02.078266image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:02.173013image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8763
98.5%
1 135
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 8763
98.5%
1 135
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8763
98.5%
1 135
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8763
98.5%
1 135
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8763
98.5%
1 135
 
1.5%

was_top5_last_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8708 
1
 
190

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8708
97.9%
1 190
 
2.1%

Length

2024-08-12T21:04:02.274742image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:02.378465image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8708
97.9%
1 190
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 8708
97.9%
1 190
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8708
97.9%
1 190
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8708
97.9%
1 190
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8708
97.9%
1 190
 
2.1%

was_top5_last_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8642 
1
 
256

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8642
97.1%
1 256
 
2.9%

Length

2024-08-12T21:04:02.483184image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:02.578251image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8642
97.1%
1 256
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 8642
97.1%
1 256
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8642
97.1%
1 256
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8642
97.1%
1 256
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8642
97.1%
1 256
 
2.9%

was_top5_last_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8575 
1
 
323

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Length

2024-08-12T21:04:02.678982image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:02.776721image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

was_top8_last_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8800 
1
 
98

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8800
98.9%
1 98
 
1.1%

Length

2024-08-12T21:04:02.873461image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:02.965249image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8800
98.9%
1 98
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 8800
98.9%
1 98
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8800
98.9%
1 98
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8800
98.9%
1 98
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8800
98.9%
1 98
 
1.1%

was_top8_last_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8681 
1
 
217

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8681
97.6%
1 217
 
2.4%

Length

2024-08-12T21:04:03.058749image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:03.146514image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8681
97.6%
1 217
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 8681
97.6%
1 217
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8681
97.6%
1 217
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8681
97.6%
1 217
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8681
97.6%
1 217
 
2.4%

was_top8_last_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8585 
1
 
313

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8585
96.5%
1 313
 
3.5%

Length

2024-08-12T21:04:03.240568image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:03.327747image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8585
96.5%
1 313
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 8585
96.5%
1 313
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8585
96.5%
1 313
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8585
96.5%
1 313
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8585
96.5%
1 313
 
3.5%

was_top8_last_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8493 
1
 
405

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8493
95.4%
1 405
 
4.6%

Length

2024-08-12T21:04:03.421703image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:03.512460image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8493
95.4%
1 405
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 8493
95.4%
1 405
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8493
95.4%
1 405
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8493
95.4%
1 405
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8493
95.4%
1 405
 
4.6%

was_top8_last_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8393 
1
 
505

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8393
94.3%
1 505
 
5.7%

Length

2024-08-12T21:04:03.618178image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:03.721901image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8393
94.3%
1 505
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 8393
94.3%
1 505
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8393
94.3%
1 505
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8393
94.3%
1 505
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8393
94.3%
1 505
 
5.7%

was_top10_last_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8731 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8731
98.1%
1 167
 
1.9%

Length

2024-08-12T21:04:03.819639image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:03.911394image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8731
98.1%
1 167
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 8731
98.1%
1 167
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8731
98.1%
1 167
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8731
98.1%
1 167
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8731
98.1%
1 167
 
1.9%

was_top10_last_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8575 
1
 
323

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Length

2024-08-12T21:04:04.012124image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:04.113852image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8575
96.4%
1 323
 
3.6%

was_top10_last_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8441 
1
 
457

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8441
94.9%
1 457
 
5.1%

Length

2024-08-12T21:04:04.233535image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:04.339250image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8441
94.9%
1 457
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 8441
94.9%
1 457
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8441
94.9%
1 457
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8441
94.9%
1 457
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8441
94.9%
1 457
 
5.1%

was_top10_last_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8325 
1
 
573

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8325
93.6%
1 573
 
6.4%

Length

2024-08-12T21:04:04.445965image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:04.542705image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8325
93.6%
1 573
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 8325
93.6%
1 573
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8325
93.6%
1 573
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8325
93.6%
1 573
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8325
93.6%
1 573
 
6.4%

was_top10_last_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8172 
1
 
726

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8172
91.8%
1 726
 
8.2%

Length

2024-08-12T21:04:04.639448image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:04.738183image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8172
91.8%
1 726
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 8172
91.8%
1 726
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8172
91.8%
1 726
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8172
91.8%
1 726
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8172
91.8%
1 726
 
8.2%

was_top15_last_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8583 
1
 
315

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8583
96.5%
1 315
 
3.5%

Length

2024-08-12T21:04:04.849885image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:04.974551image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8583
96.5%
1 315
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 8583
96.5%
1 315
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8583
96.5%
1 315
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8583
96.5%
1 315
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8583
96.5%
1 315
 
3.5%

was_top15_last_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8323 
1
 
575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8323
93.5%
1 575
 
6.5%

Length

2024-08-12T21:04:05.071292image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:05.173872image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8323
93.5%
1 575
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 8323
93.5%
1 575
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8323
93.5%
1 575
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8323
93.5%
1 575
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8323
93.5%
1 575
 
6.5%

was_top15_last_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
8120 
1
 
778

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8120
91.3%
1 778
 
8.7%

Length

2024-08-12T21:04:05.321479image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:05.449151image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8120
91.3%
1 778
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 8120
91.3%
1 778
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8120
91.3%
1 778
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8120
91.3%
1 778
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8120
91.3%
1 778
 
8.7%

was_top15_last_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
7876 
1
1022 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7876
88.5%
1 1022
 
11.5%

Length

2024-08-12T21:04:05.576809image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:05.701477image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 7876
88.5%
1 1022
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 7876
88.5%
1 1022
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7876
88.5%
1 1022
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7876
88.5%
1 1022
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7876
88.5%
1 1022
 
11.5%

was_top15_last_5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size434.6 KiB
0
7609 
1
1289 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7609
85.5%
1 1289
 
14.5%

Length

2024-08-12T21:04:05.830132image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:04:05.950080image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 7609
85.5%
1 1289
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 7609
85.5%
1 1289
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7609
85.5%
1 1289
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7609
85.5%
1 1289
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7609
85.5%
1 1289
 
14.5%

Position
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.020004
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:04:06.069759image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum24
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1297869
Coefficient of variation (CV)0.55624178
Kurtosis-1.1203658
Mean11.020004
Median Absolute Deviation (MAD)5
Skewness0.0552226
Sum98056
Variance37.574288
MonotonicityNot monotonic
2024-08-12T21:04:06.185291image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 425
 
4.8%
11 425
 
4.8%
18 425
 
4.8%
17 425
 
4.8%
16 425
 
4.8%
15 425
 
4.8%
14 425
 
4.8%
2 425
 
4.8%
12 425
 
4.8%
13 425
 
4.8%
Other values (14) 4648
52.2%
ValueCountFrequency (%)
1 425
4.8%
2 425
4.8%
3 425
4.8%
4 425
4.8%
5 425
4.8%
6 425
4.8%
7 425
4.8%
8 425
4.8%
9 425
4.8%
10 425
4.8%
ValueCountFrequency (%)
24 50
 
0.6%
23 58
 
0.7%
22 153
 
1.7%
21 159
 
1.8%
20 410
4.6%
19 418
4.7%
18 425
4.8%
17 425
4.8%
16 425
4.8%
15 425
4.8%

Interactions

2024-08-12T21:03:50.077165image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:41.829084image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.770945image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.626822image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.516891image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.440893image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.341831image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.255637image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.204796image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.124540image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.162936image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:41.919842image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.855606image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.716581image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.608645image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.526664image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.431567image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.350384image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.294382image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.218290image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.243719image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.001624image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.932400image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.799360image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.693432image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.612436image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.513467image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.448122image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.378158image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.305707image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.327499image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.091383image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.017173image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.883314image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.780930image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.698205image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.599912image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.543423image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.471908image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.397460image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.422246image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.181142image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.103940image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.974099image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.869998image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.791955image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.689670image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.642160image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.563608image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.495366image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.509013image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.267910image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.188715image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.060194image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.962750image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.879719image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.775442image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.733230image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.657358image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.592115image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.593814image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.358672image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.271770image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.145965image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.053479image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.965490image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.860693image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.824001image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.746120image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.688736image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.688562image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.454417image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.362527image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.241709image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.155208image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.061235image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.963420image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.920036image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.842861image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.787938image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.778345image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.585067image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.449295image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.333899image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.247958image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.159970image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.066144image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.015780image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.936611image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.884679image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:50.873092image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:42.682806image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:43.544044image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:44.431640image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:45.349686image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:46.255983image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:47.168869image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:48.115431image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.036345image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:49.985411image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2024-08-12T21:04:06.315942image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
CircuitIDConstructorIDConstructorNamePermanentNumberPositionRoundSeasonavg_Q1_timeavg_Q2_timeavg_Q3_timeavg_constructor_positionconstructor_nationalitydriver_avg_positiondriver_nationalitydriver_total_raceswas_first_last_1was_first_last_2was_first_last_3was_first_last_4was_first_last_5was_top10_last_1was_top10_last_2was_top10_last_3was_top10_last_4was_top10_last_5was_top15_last_1was_top15_last_2was_top15_last_3was_top15_last_4was_top15_last_5was_top3_last_1was_top3_last_2was_top3_last_3was_top3_last_4was_top3_last_5was_top5_last_1was_top5_last_2was_top5_last_3was_top5_last_4was_top5_last_5was_top8_last_1was_top8_last_2was_top8_last_3was_top8_last_4was_top8_last_5
CircuitID1.0000.0540.0540.0720.0000.6590.2850.7040.7010.6840.0670.0490.1780.0000.2250.1370.1180.1020.1260.1320.3860.3620.3540.3720.3430.4370.4540.4370.4500.4110.2360.1940.1970.2070.1930.3020.2820.2660.2820.2590.3530.3320.3250.3430.322
ConstructorID0.0541.0001.0000.4080.3590.0140.4320.0700.0750.0750.9980.9990.2310.3850.2030.0740.0590.0680.1170.1190.0780.1050.1320.1540.1760.0400.0700.1040.1270.1470.0620.0890.1320.1570.1960.0940.1410.1860.2180.2480.0870.1140.1490.1810.206
ConstructorName0.0541.0001.0000.4080.3590.0140.4320.0700.0750.0750.9980.9990.2310.3850.2030.0740.0590.0680.1170.1190.0780.1050.1320.1540.1760.0400.0700.1040.1270.1470.0620.0890.1320.1570.1960.0940.1410.1860.2180.2480.0870.1140.1490.1810.206
PermanentNumber0.0720.4080.4081.000-0.1580.0560.5760.0010.002-0.020-0.1350.295-0.0710.5810.1790.1510.1150.1440.1870.1440.0870.1130.1450.1820.2080.0290.0480.0700.0910.1110.0920.1090.1130.1620.1830.0960.1490.1940.2320.2580.1050.1330.1730.2110.241
Position0.0000.3590.359-0.1581.000-0.004-0.0360.0010.0010.0130.6720.2930.4140.201-0.3590.0370.0420.0460.0740.0810.0800.1060.1280.1440.1630.0530.0750.0950.1210.1400.0530.0810.1090.1220.1510.0830.1170.1470.1690.1880.0710.1030.1220.1410.158
Round0.6590.0140.0140.056-0.0041.0000.0830.0630.0660.053-0.0080.0000.0040.000-0.0180.0490.0610.0520.0430.0300.1410.1750.1900.1830.1470.1830.2350.2470.2420.1970.0860.0990.0960.0920.0660.1140.1380.1430.1290.1000.1310.1630.1810.1720.142
Season0.2850.4320.4320.576-0.0360.0831.000-0.045-0.046-0.069-0.0930.2440.0230.2810.0860.0190.0390.0430.0580.0410.0330.0480.0730.1120.1210.0140.0300.0570.0940.0990.0320.0410.0570.0840.0800.0150.0440.0670.0970.1030.0460.0580.0840.1230.138
avg_Q1_time0.7040.0700.0700.0010.0010.063-0.0451.0000.9910.983-0.0060.0460.0040.0980.0410.2060.1630.1190.1390.1180.4400.3150.2450.2960.2650.4810.3620.2900.3460.3150.3270.2410.1920.2010.1780.4210.3270.2720.2830.2560.4430.3140.2500.2970.276
avg_Q2_time0.7010.0750.0750.0020.0010.066-0.0460.9911.0000.995-0.0050.0450.0040.0980.0360.1860.1470.1060.1300.1130.4560.3350.2630.3030.2780.4890.3770.3130.3520.3360.2990.2180.1720.1830.1630.3820.2970.2440.2590.2390.4310.3040.2400.2860.269
avg_Q3_time0.6840.0750.075-0.0200.0130.053-0.0690.9830.9951.0000.0070.0500.0250.1130.0180.1940.1530.1130.1360.1160.3750.2820.2180.2780.2620.3960.3150.2510.3210.3040.3090.2260.1800.1910.1680.3960.3060.2530.2690.2460.4230.3010.2360.2880.272
avg_constructor_position0.0670.9980.998-0.1350.672-0.008-0.093-0.006-0.0050.0071.0000.6460.3960.391-0.3650.0400.0280.0400.0640.0690.0740.0950.1070.1280.1460.0510.0740.0940.1170.1330.0590.0720.0920.1050.1270.0710.1000.1250.1480.1680.0710.0900.1040.1310.149
constructor_nationality0.0490.9990.9990.2950.2930.0000.2440.0460.0450.0500.6461.0000.1700.4060.1560.0900.0660.0800.1240.1190.0580.0820.1000.1180.1400.0430.0560.0780.0960.1150.0660.0910.1180.1390.1720.0860.1200.1570.1840.2100.0790.0910.1160.1430.165
driver_avg_position0.1780.2310.231-0.0710.4140.0040.0230.0040.0040.0250.3960.1701.0000.299-0.4210.0820.0610.0620.1040.1140.1160.0910.1280.1390.1270.0810.0640.1010.0950.0920.0520.0750.0820.1150.1600.1040.1190.1480.1880.2210.0920.0890.1220.1470.156
driver_nationality0.0000.3850.3850.5810.2010.0000.2810.0980.0980.1130.3910.4060.2991.0000.2760.1670.1300.1090.1880.1580.0990.1290.1700.1920.2050.0670.0960.1390.1480.1600.1610.1570.1420.1790.2080.1250.1390.1870.2190.2510.1140.1460.1760.2170.240
driver_total_races0.2250.2030.2030.179-0.359-0.0180.0860.0410.0360.018-0.3650.156-0.4210.2761.0000.1110.0900.0760.1480.1300.2200.2040.2610.2750.3150.1400.1690.2170.2130.2390.1900.1650.2370.2440.3330.2820.2740.3590.3530.4090.2470.2160.2740.2740.321
was_first_last_10.1370.0740.0740.1510.0370.0490.0190.2060.1860.1940.0400.0900.0820.1670.1111.0000.7510.5670.4680.3660.2300.1640.1360.1210.1060.1660.1200.1020.0870.0760.4800.3550.2820.2460.2100.3450.2560.2150.1850.1640.3020.2010.1660.1450.129
was_first_last_20.1180.0590.0590.1150.0420.0610.0390.1630.1470.1530.0280.0660.0610.1300.0900.7511.0000.7320.6040.4720.1800.2110.1760.1560.1370.1280.1560.1320.1130.0990.3790.4580.3640.3180.2710.2720.3310.2780.2380.2110.2370.2600.2150.1880.167
was_first_last_30.1020.0680.0680.1440.0460.0520.0430.1190.1060.1130.0400.0800.0620.1090.0760.5670.7321.0000.8110.6330.1320.1550.2370.2100.1850.0920.1110.1780.1520.1330.2850.3440.4890.4270.3640.2030.2470.3730.3200.2840.1760.1920.2890.2520.224
was_first_last_40.1260.1170.1170.1870.0740.0430.0580.1390.1300.1360.0640.1240.1040.1880.1480.4680.6040.8111.0000.7710.1060.1240.1910.2560.2250.0710.0860.1400.1860.1630.2340.2830.4020.5200.4430.1660.2020.3050.3900.3460.1430.1560.2340.3070.274
was_first_last_50.1320.1190.1190.1440.0810.0300.0410.1180.1130.1160.0690.1190.1140.1580.1300.3660.4720.6330.7711.0000.0780.0900.1410.1910.2900.0490.0580.0990.1330.2100.1810.2180.3110.4030.5700.1270.1540.2340.3000.4450.1090.1160.1770.2330.352
was_top10_last_10.3860.0780.0780.0870.0800.1410.0330.4400.4560.3750.0740.0580.1160.0990.2200.2300.1800.1320.1060.0781.0000.7100.5920.5250.4620.7200.5240.4450.3830.3350.4730.3460.2700.2330.1940.6620.4870.4060.3450.3030.7590.5020.4120.3570.315
was_top10_last_20.3620.1050.1050.1130.1060.1750.0480.3150.3350.2820.0950.0820.0910.1290.2040.1640.2110.1550.1240.0900.7101.0000.8330.7390.6500.5040.7370.6260.5380.4710.3370.4590.3580.3080.2560.4720.6370.5300.4500.3940.5410.8130.6690.5820.515
was_top10_last_30.3540.1320.1320.1450.1280.1900.0730.2450.2630.2180.1070.1000.1280.1700.2610.1360.1760.2370.1910.1410.5920.8331.0000.8860.7800.4140.6070.7510.6450.5650.2810.3820.4830.4170.3480.3930.5310.6330.5370.4710.4510.6780.8190.7130.631
was_top10_last_40.3720.1540.1540.1820.1440.1830.1120.2960.3030.2780.1280.1180.1390.1920.2750.1210.1560.2100.2560.1910.5250.7390.8861.0000.8790.3670.5350.6620.7280.6370.2490.3390.4280.4900.4100.3490.4710.5610.6550.5740.4000.6010.7270.8310.736
was_top10_last_50.3430.1760.1760.2080.1630.1470.1210.2650.2780.2620.1460.1400.1270.2050.3150.1060.1370.1850.2250.2900.4620.6500.7800.8791.0000.3170.4630.5740.6300.7240.2190.2980.3770.4320.5070.3070.4150.4940.5760.6500.3520.5290.6390.7320.822
was_top15_last_10.4370.0400.0400.0290.0530.1830.0140.4810.4890.3960.0510.0430.0810.0670.1400.1660.1280.0920.0710.0490.7200.5040.4140.3670.3171.0000.7280.6180.5310.4640.3420.2460.1880.1590.1280.4780.3470.2850.2380.2050.5480.3540.2850.2480.214
was_top15_last_20.4540.0700.0700.0480.0750.2350.0300.3620.3770.3150.0740.0560.0640.0960.1690.1200.1560.1110.0860.0580.5240.7370.6070.5350.4630.7281.0000.8480.7290.6380.2490.3380.2590.2190.1770.3480.4700.3860.3220.2780.3990.6000.4870.4220.367
was_top15_last_30.4370.1040.1040.0700.0950.2470.0570.2900.3130.2510.0940.0780.1010.1390.2170.1020.1320.1780.1400.0990.4450.6260.7510.6620.5740.6180.8481.0000.8590.7510.2110.2870.3630.3090.2520.2960.3990.4760.3980.3430.3390.5090.6160.5330.465
was_top15_last_40.4500.1270.1270.0910.1210.2420.0940.3460.3520.3210.1170.0960.0950.1480.2130.0870.1130.1520.1860.1330.3830.5380.6450.7280.6300.5310.7290.8591.0000.8750.1810.2470.3120.3570.2910.2540.3430.4090.4770.4110.2910.4380.5290.6050.528
was_top15_last_50.4110.1470.1470.1110.1400.1970.0990.3150.3360.3040.1330.1150.0920.1600.2390.0760.0990.1330.1630.2100.3350.4710.5650.6370.7240.4640.6380.7510.8751.0000.1580.2160.2730.3120.3670.2220.3000.3580.4170.4710.2550.3830.4630.5300.595
was_top3_last_10.2360.0620.0620.0920.0530.0860.0320.3270.2990.3090.0590.0660.0520.1610.1900.4800.3790.2850.2340.1810.4730.3370.2810.2490.2190.3420.2490.2110.1810.1581.0000.7300.5800.5070.4320.7100.5280.4430.3800.3370.6210.4140.3430.3000.267
was_top3_last_20.1940.0890.0890.1090.0810.0990.0410.2410.2180.2260.0720.0910.0750.1570.1650.3550.4580.3440.2830.2180.3460.4590.3820.3390.2980.2460.3380.2870.2470.2160.7301.0000.7890.6890.5870.5240.7170.6030.5170.4590.4560.5630.4660.4080.363
was_top3_last_30.1970.1320.1320.1130.1090.0960.0570.1920.1720.1800.0920.1180.0820.1420.2370.2820.3640.4890.4020.3110.2700.3580.4830.4280.3770.1880.2590.3630.3120.2730.5800.7891.0000.8700.7410.4140.5670.7610.6530.5790.3600.4430.5890.5150.458
was_top3_last_40.2070.1570.1570.1620.1220.0920.0840.2010.1830.1910.1050.1390.1150.1790.2440.2460.3180.4270.5200.4030.2330.3080.4170.4900.4320.1590.2190.3090.3570.3120.5070.6890.8701.0000.8490.3610.4940.6630.7480.6630.3130.3840.5110.5890.525
was_top3_last_50.1930.1960.1960.1830.1510.0660.0800.1780.1630.1680.1270.1720.1600.2080.3330.2100.2710.3640.4430.5700.1940.2560.3480.4100.5070.1280.1770.2520.2910.3670.4320.5870.7410.8491.0000.3050.4180.5620.6340.7790.2630.3220.4300.4960.616
was_top5_last_10.3020.0940.0940.0960.0830.1140.0150.4210.3820.3960.0710.0860.1040.1250.2820.3450.2720.2030.1660.1270.6620.4720.3930.3490.3070.4780.3480.2960.2540.2220.7100.5240.4140.3610.3051.0000.7380.6200.5320.4720.8680.5790.4790.4190.373
was_top5_last_20.2820.1410.1410.1490.1170.1380.0440.3270.2970.3060.1000.1200.1190.1390.2740.2560.3310.2470.2020.1540.4870.6370.5310.4710.4150.3470.4700.3990.3430.3000.5280.7170.5670.4940.4180.7381.0000.8370.7180.6370.6430.7820.6470.5660.504
was_top5_last_30.2660.1860.1860.1940.1470.1430.0670.2720.2440.2530.1250.1570.1480.1870.3590.2150.2780.3730.3050.2340.4060.5300.6330.5610.4940.2850.3860.4760.4090.3580.4430.6030.7610.6630.5620.6200.8371.0000.8560.7590.5390.6540.7710.6750.600
was_top5_last_40.2820.2180.2180.2320.1690.1290.0970.2830.2590.2690.1480.1840.1880.2190.3530.1850.2380.3200.3900.3000.3450.4500.5370.6550.5760.2380.3220.3980.4770.4170.3800.5170.6530.7480.6340.5320.7180.8561.0000.8850.4620.5590.6590.7870.700
was_top5_last_50.2590.2480.2480.2580.1880.1000.1030.2560.2390.2460.1680.2100.2210.2510.4090.1640.2110.2840.3460.4450.3030.3940.4710.5740.6500.2050.2780.3430.4110.4710.3370.4590.5790.6630.7790.4720.6370.7590.8851.0000.4080.4930.5810.6940.790
was_top8_last_10.3530.0870.0870.1050.0710.1310.0460.4430.4310.4230.0710.0790.0920.1140.2470.3020.2370.1760.1430.1090.7590.5410.4510.4000.3520.5480.3990.3390.2910.2550.6210.4560.3600.3130.2630.8680.6430.5390.4620.4081.0000.6640.5500.4810.428
was_top8_last_20.3320.1140.1140.1330.1030.1630.0580.3140.3040.3010.0900.0910.0890.1460.2160.2010.2600.1920.1560.1160.5020.8130.6780.6010.5290.3540.6000.5090.4380.3830.4140.5630.4430.3840.3220.5790.7820.6540.5590.4930.6641.0000.8260.7220.643
was_top8_last_30.3250.1490.1490.1730.1220.1810.0840.2500.2400.2360.1040.1160.1220.1760.2740.1660.2150.2890.2340.1770.4120.6690.8190.7270.6390.2850.4870.6160.5290.4630.3430.4660.5890.5110.4300.4790.6470.7710.6590.5810.5500.8261.0000.8730.777
was_top8_last_40.3430.1810.1810.2110.1410.1720.1230.2970.2860.2880.1310.1430.1470.2170.2740.1450.1880.2520.3070.2330.3570.5820.7130.8310.7320.2480.4220.5330.6050.5300.3000.4080.5150.5890.4960.4190.5660.6750.7870.6940.4810.7220.8731.0000.889
was_top8_last_50.3220.2060.2060.2410.1580.1420.1380.2760.2690.2720.1490.1650.1560.2400.3210.1290.1670.2240.2740.3520.3150.5150.6310.7360.8220.2140.3670.4650.5280.5950.2670.3630.4580.5250.6160.3730.5040.6000.7000.7900.4280.6430.7770.8891.000

Missing values

2024-08-12T21:03:51.090510image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T21:03:51.443567image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-12T21:03:51.778982image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SeasonRoundCircuitIDDriverIDCodePermanentNumberGivenNameFamilyNameDateOfBirthdriver_nationalityConstructorIDConstructorNameconstructor_nationalityQ1Q2Q3avg_Q1_timeavg_Q2_timeavg_Q3_timedriver_avg_positiondriver_total_racesavg_constructor_positionwas_first_last_1was_first_last_2was_first_last_3was_first_last_4was_first_last_5was_top3_last_1was_top3_last_2was_top3_last_3was_top3_last_4was_top3_last_5was_top5_last_1was_top5_last_2was_top5_last_3was_top5_last_4was_top5_last_5was_top8_last_1was_top8_last_2was_top8_last_3was_top8_last_4was_top8_last_5was_top10_last_1was_top10_last_2was_top10_last_3was_top10_last_4was_top10_last_5was_top15_last_1was_top15_last_2was_top15_last_3was_top15_last_4was_top15_last_5Position
020001albert_parkhakkinenNaN0MikaHäkkinen1968-09-28FinnishmclarenMcLarenBritish1:30.55600NaNNaNNaNNaNNaN8.4592680000100001000010000100001000011
120001albert_parkcoulthardCOU0DavidCoulthard1971-03-27BritishmclarenMcLarenBritish1:30.9100086.381086.06389.0410008.0000001.08.4592680000000000000000000000000000002
220001albert_parkmichael_schumacherMSC0MichaelSchumacher1969-01-03GermanferrariFerrariItalian1:31.0750085.968585.22185.1315005.5000002.05.8270590000000000000000000000000000003
320001albert_parkbarrichelloBAR0RubensBarrichello1972-05-23BrazilianferrariFerrariItalian1:31.1020085.354084.93485.8610005.0000002.05.8270590000000000000000000000000000004
420001albert_parkfrentzenNaN0Heinz-HaraldFrentzen1967-05-18GermanjordanJordanIrish1:31.35900NaNNaNNaNNaNNaN15.4750000000000000000000000000000000005
520001albert_parktrulliTRU0JarnoTrulli1974-07-13ItalianjordanJordanIrish1:31.5040086.545086.01888.0193337.3333333.015.4750000000000000000000000000000000006
620001albert_parkirvineNaN0EddieIrvine1965-11-10BritishjaguarJaguarBritish1:31.51400NaNNaNNaNNaNNaN12.1829270000000000000000000000000000007
720001albert_parkvilleneuveVIL0JacquesVilleneuve1971-04-09CanadianbarBARBritish1:31.9680088.460086.71489.2390009.0000001.09.3448280000000000000000000000000000008
820001albert_parkfisichellaFIS0GiancarloFisichella1973-01-14ItalianbenettonBenettonItalian1:31.9920087.517586.07086.6345004.0000002.013.0000000000000000000000000000000000009
920001albert_parksaloNaN0MikaSalo1966-11-30FinnishsauberSauberSwiss1:32.01800NaNNaNNaNNaNNaN14.39479000000000000000000000000000000010
SeasonRoundCircuitIDDriverIDCodePermanentNumberGivenNameFamilyNameDateOfBirthdriver_nationalityConstructorIDConstructorNameconstructor_nationalityQ1Q2Q3avg_Q1_timeavg_Q2_timeavg_Q3_timedriver_avg_positiondriver_total_racesavg_constructor_positionwas_first_last_1was_first_last_2was_first_last_3was_first_last_4was_first_last_5was_top3_last_1was_top3_last_2was_top3_last_3was_top3_last_4was_top3_last_5was_top5_last_1was_top5_last_2was_top5_last_3was_top5_last_4was_top5_last_5was_top8_last_1was_top8_last_2was_top8_last_3was_top8_last_4was_top8_last_5was_top10_last_1was_top10_last_2was_top10_last_3was_top10_last_4was_top10_last_5was_top15_last_1was_top15_last_2was_top15_last_3was_top15_last_4was_top15_last_5Position
8888202414spaalbonALB23AlexanderAlbon1996-03-23ThaiwilliamsWilliamsBritish1:55.7221:54.4730104.545000103.934000104.0505007.0000002.012.40094300000000000000000000000001111111
8889202414spagaslyGAS10PierreGasly1996-02-07FrenchalpineAlpine F1 TeamFrench1:54.9111:54.6350120.387000116.440000121.1640006.0000001.011.25000000000000000000000000000001111112
8890202414sparicciardoRIC3DanielRicciardo1989-07-01AustralianrbRB F1 TeamItalian1:55.4511:54.6820110.916250109.640625112.2187505.5000008.012.21428600000000000000000000000001111113
8891202414spabottasBOT77ValtteriBottas1989-08-28FinnishsauberSauberSwiss1:55.5311:54.7640111.321286110.151857110.7102864.8571437.014.39479000000000000000000000000001111114
8892202414spastrollSTR18LanceStroll1998-10-29Canadianaston_martinAston MartinBritish1:56.0721:55.7160111.464000107.342000105.7220009.5000002.012.22500000000000000000000000000001111115
8893202414spahulkenbergHUL27NicoHülkenberg1987-08-19GermanhaasHaas F1 TeamAmerican1:56.30800108.675250106.168750106.0300007.5000004.014.08078000000000000000000000000000000016
8894202414spakevin_magnussenMAG20KevinMagnussen1992-10-05DanishhaasHaas F1 TeamAmerican1:56.50000113.584667112.319667119.5660008.6666673.014.08078000000000000000000000000000000017
8895202414spatsunodaTSU22YukiTsunoda2000-05-11JapaneserbRB F1 TeamItalian1:56.59300NaNNaNNaNNaNNaN12.21428600000000000000000000000000000018
8896202414spasargeantSAR2LoganSargeant2000-12-31AmericanwilliamsWilliamsBritish1:57.23000NaNNaNNaNNaNNaN12.40094300000000000000000000000000000019
8897202414spazhouZHO24GuanyuZhou1999-05-30ChinesesauberSauberSwiss1:57.77500NaNNaNNaNNaNNaN14.39479000000000000000000000000000000020